{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#作图\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1.0/(1+np.e**(-x))\n",
    "x = np.linspace(-2, 2,66)\n",
    "y = sigmoid(-4*x+3)/sigmoid(3)\n",
    "plt.plot(x, y)\n",
    "plt.grid()  # 生成网格\n",
    "plt.show()\n",
    "\n",
    "y = sigmoid\n",
    "plt.plot(x, y(x)*(1-y(x)) )\n",
    "plt.grid()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据XY 坐标作图\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.array([1,2,3,4,5,6,7,8])\n",
    "y = np.array([3,5,7,6,2,6,10,15])\n",
    "plt.plot(x,y,'r')# 折线 1 x 2 y 3 color\n",
    "plt.plot(x,y,'g')# 4 line w\n",
    "x = np.array([1,2,3,4,5,6,7,8])\n",
    "y = np.array([13,25,17,36,21,16,10,15])\n",
    "plt.bar(x,y,0.2,alpha=1,color='b')# 5 color 4 透明度 3 0.9\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# seaborn [参考](https://blog.csdn.net/qq_33120943/article/details/76569756)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!conda install seaborn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Type_1</th>\n",
       "      <th>Type_2</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>525</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>405</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name Type_1  Type_2  Total  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "#                                                                            \n",
       "1   Bulbasaur  Grass  Poison    318  45      49       49       65       65   \n",
       "2     Ivysaur  Grass  Poison    405  60      62       63       80       80   \n",
       "3    Venusaur  Grass  Poison    525  80      82       83      100      100   \n",
       "4  Charmander   Fire     NaN    309  39      52       43       60       50   \n",
       "5  Charmeleon   Fire     NaN    405  58      64       58       80       65   \n",
       "\n",
       "   Speed  Stage  Legendary  \n",
       "#                           \n",
       "1     45      1      False  \n",
       "2     60      2      False  \n",
       "3     80      3      False  \n",
       "4     65      1      False  \n",
       "5     80      2      False  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as  plt\n",
    "import seaborn as  sns\n",
    "%matplotlib inline\n",
    "df=pd.read_excel(\"Pokemon.xlsx\",index_col=0)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23b23c60080>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.scatter(df.Total,df.Speed)\n",
    "fig, ax = plt.subplots(figsize=(16,10), dpi= 80) \n",
    "sns.stripplot(df.Speed,df.Total,size=df.Attack/5, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'total'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-39d76fa6ebe6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtotal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Type_1\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   4374\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4375\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4376\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4377\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4378\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'total'"
     ]
    }
   ],
   "source": [
    "df.groupby(\"Type_1\").plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 箱图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ea58198f28>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHMpJREFUeJzt3X+UXGWd5/H3Jz8wCQFCSAcwnRA1EXVdBOwD/pxVMBzDegyekQVm1NbDTtSFxJHxOOzRnQH17IDjLtq4g8uA2syqyPpjyUFQsgirjsuPQEKARO2WDaQMkHQgmcQOkCbf/eM+DUVSnbrVVdVVdfN5ndOn7n3quU99763b3/vcp27VVURgZmbFNanVAZiZWXM50ZuZFZwTvZlZwTnRm5kVnBO9mVnBOdGbmRWcE72ZWcE50ZuZFZwTvZlZwU1pdQAAc+bMiYULF7Y6DDOzjnL//fcPRURXtXpVE72kE4HvlxW9Gvgb4IZUvhDYBPy7iHhGkoCvAWcDw8BHI+KBg73GwoULWbNmTbVQzMysjKTH8tSrOnQTEb+NiJMj4mTgzWTJ+8fApcAdEbEYuCPNAywFFqe/5cA1tYdvZmaNUusY/ZnA7yPiMWAZ0J/K+4Fz0vQy4IbI3A3MknR8Q6I1M7Oa1Zrozwe+l6aPjYgnANLj3FQ+D9hctkwplb2MpOWS1khas23bthrDMDOzvHInekmHAe8H/me1qhXKDvgt5Ii4NiJ6IqKnq6vqZwlmZjZOtfTolwIPRMRTaf6p0SGZ9Lg1lZeA+WXLdQNb6g3UzMzGp5ZEfwEvDdsArAJ603QvcHNZ+UeUeQuwc3SIx8zMJl6u6+glzQCWAB8vK74CuEnShcDjwLmp/FaySysHya7Q+VjDojUzs5rlSvQRMQwcs1/ZdrKrcPavG8BFDYnuIPr6+hgcHKxar1QqAdDd3V217qJFi1i5cmXdsZmZtZO2+GZsM+3Zs6fVIZiZtVTHJvq8Pe/Ren19fc0Mx8ysbflHzczMCs6J3sys4JzozcwKzonezKzgnOjNzArOid7MrOCc6M3MCs6J3sys4JzozcwKzonezKzgnOjNzArOid7MrOCc6M3MCs6J3sys4JzozcwKzonezKzgnOjNzArOid7MrOByJXpJsyT9QNJvJG2U9FZJsyWtljSQHo9OdSWpT9KgpPWSTm3uKpiZ2cHk7dF/DfhpRLwOeBOwEbgUuCMiFgN3pHmApcDi9LccuKahEZuZWU2qJnpJRwJ/AlwPEBHPR8QOYBnQn6r1A+ek6WXADZG5G5gl6fiGR25mZrnk6dG/GtgGfEvSWknXSTocODYingBIj3NT/XnA5rLlS6nMzMxaIE+inwKcClwTEacAf+SlYZpKVKEsDqgkLZe0RtKabdu25QrWzMxqlyfRl4BSRNyT5n9AlvifGh2SSY9by+rPL1u+G9iyf6MRcW1E9ERET1dX13jjNzOzKqom+oh4Etgs6cRUdCawAVgF9KayXuDmNL0K+Ei6+uYtwM7RIR4zM5t4U3LWWwF8R9JhwKPAx8gOEjdJuhB4HDg31b0VOBsYBIZTXTMza5FciT4i1gE9FZ46s0LdAC6qMy4zM2sQfzPWzKzgnOjNzArOid7MrOCc6M3MCs6J3sys4JzozcwKzonezKzgnOjNzArOid7MrOCc6M3MCs6J3sys4JzozcwKzonezKzgnOjNzArOid7MrOCc6M3MCs6J3sys4JzozcwKzonezKzgnOjNzAouV6KXtEnSQ5LWSVqTymZLWi1pID0encolqU/SoKT1kk5t5gqYmdnB1dKjf3dEnBwRPWn+UuCOiFgM3JHmAZYCi9PfcuCaRgVrZma1q2foZhnQn6b7gXPKym+IzN3ALEnH1/E6ZmZWh7yJPoDbJd0vaXkqOzYingBIj3NT+Txgc9mypVRmZmYtMCVnvbdHxBZJc4HVkn5zkLqqUBYHVMoOGMsBFixYkDMMMzOrVa4efURsSY9bgR8DpwFPjQ7JpMetqXoJmF+2eDewpUKb10ZET0T0dHV1jX8NzMzsoKr26CUdDkyKiF1p+izgC8AqoBe4Ij3enBZZBVws6UbgdGDn6BBPXn19fQwODtayyJgGBgYAWLlyZUPaW7RoUcPaMjObCHmGbo4FfixptP53I+Knku4DbpJ0IfA4cG6qfytwNjAIDAMfqzWowcFB1j60gX0zZte66AH0fDZqdP/vn6y7rUnDT9fdhpnZRKua6CPiUeBNFcq3A2dWKA/gonoD2zdjNs++4X31NtNQ0zbc0uoQzMxq5m/GmpkVnBO9mVnBOdGbmRWcE72ZWcE50ZuZFZwTvZlZwTnRm5kVnBO9mVnBOdGbmRWcE72ZWcE50ZuZFZwTvZlZwTnRm5kVnBO9mVnBOdGbmRWcE72ZWcE50ZuZFZwTvZlZwTnRm5kVnBO9mVnBOdGbmRVc7kQvabKktZJuSfOvknSPpAFJ35d0WCp/RZofTM8vbE7oZmaWx5Qa6n4K2AgcmeavBK6KiBslfQO4ELgmPT4TEYsknZ/qnVdLUKVSiUnDO5m24ZZaFmu6ScPbKZVGWh2GmVlNcvXoJXUD/xa4Ls0LOAP4QarSD5yTppeledLzZ6b6ZmbWAnl79F8FPgsckeaPAXZExGj3tgTMS9PzgM0AETEiaWeqP1TeoKTlwHKABQsWvOzFuru7eeq5KTz7hvfVtDLNNm3DLXR3H9fqMMzMalK1Ry/pfcDWiLi/vLhC1cjx3EsFEddGRE9E9HR1deUK1szMapenR/924P2SzgamkY3RfxWYJWlK6tV3A1tS/RIwHyhJmgIcBTzd8MjNzCyXqj36iPiPEdEdEQuB84GfR8SfA3cCH0zVeoGb0/SqNE96/ucRcUCP3szMJkY919H/NXCJpEGyMfjrU/n1wDGp/BLg0vpCNDOzetRyeSURcRdwV5p+FDitQp1ngXMbEJuZmTWAvxlrZlZwTvRmZgXnRG9mVnBO9GZmBedEb2ZWcE70ZmYF50RvZlZwTvRmZgXnRG9mVnBO9GZmBedEb2ZWcE70ZmYF50RvZlZwTvRmZgXnRG9mVnBO9GZmBedEb2ZWcDXdYWoiTRp+mmkbbqm7HT37LwDEtCPrbmvS8NPAcXW3Y2Y2kdoy0S9atKhhbQ0M7AJg8WsakaCPa2hsZmYToS0T/cqVKxveVl9fX8PaNDPrJFXH6CVNk3SvpAclPSLp8lT+Kkn3SBqQ9H1Jh6XyV6T5wfT8wuaugpmZHUyeD2OfA86IiDcBJwPvlfQW4ErgqohYDDwDXJjqXwg8ExGLgKtSPTMza5GqiT4yu9Ps1PQXwBnAD1J5P3BOml6W5knPnylJDYvYzMxqkuvySkmTJa0DtgKrgd8DOyJiJFUpAfPS9DxgM0B6fidwTCODNjOz/HIl+oh4ISJOBrqB04DXV6qWHiv13mP/AknLJa2RtGbbtm154zUzsxrV9IWpiNgB3AW8BZglafSqnW5gS5ouAfMB0vNHAU9XaOvaiOiJiJ6urq7xRW9mZlXlueqmS9KsND0deA+wEbgT+GCq1gvcnKZXpXnS8z+PiAN69GZmNjHyXEd/PNAvaTLZgeGmiLhF0gbgRklfAtYC16f61wP/JGmQrCd/fhPiNjOznKom+ohYD5xSofxRsvH6/cufBc5tSHRmZlY3/6iZmVnBOdGbmRWcE72ZWcE50ZuZFZwTvZlZwTnRt7mhoSFWrFjB9u3bWx2KmXUoJ/o219/fz/r16+nv769e2cysAif6NjY0NMRtt91GRHDbbbe5V29m4+JE38b6+/sZ/fWIffv2uVdvZuPiRN/GVq9ezd69ewHYu3cvt99+e4sjMrNO5ETfxpYsWcLUqVMBmDp1KmeddVaLIzKzTuRE38Z6e3sZvTnXpEmT6O3trbKEmdmBnOjb2Jw5c1i6dCmSWLp0Kccc4xt1mVnt8vxMsbVQb28vmzZtcm/ezMbNib7NzZkzh6uvvrrVYZhZB/PQjZlZwTnRm5kVnBO9mVnBOdGbmRWcE72ZWcFVTfSS5ku6U9JGSY9I+lQqny1ptaSB9Hh0KpekPkmDktZLOrXZK2FmZmPL06MfAf4qIl4PvAW4SNIbgEuBOyJiMXBHmgdYCixOf8uBaxoetZmZ5VY10UfEExHxQJreBWwE5gHLgNGfU+wHzknTy4AbInM3MEvS8Q2P3MzMcqlpjF7SQuAU4B7g2Ih4ArKDATA3VZsHbC5brJTKzMysBXJ/M1bSTOCHwF9GxL+M/thWpaoVyqJCe8vJhnZYsGBB3jBe1NfXx+DgYNV6AwMDAKxcubJq3UWLFuWqZ2bWSXL16CVNJUvy34mIH6Xip0aHZNLj1lReAuaXLd4NbNm/zYi4NiJ6IqKnq6trvPFXNX36dKZPn9609s3M2l3VHr2yrvv1wMaI+K9lT60CeoEr0uPNZeUXS7oROB3YOTrE00jueVuzDQ0Ncfnll3PZZZf5l0Oto+Xp0b8d+DBwhqR16e9ssgS/RNIAsCTNA9wKPAoMAv8I/IfGh23WfL4xuxVF1R59RPyKyuPuAGdWqB/ARXXGZdZS+9+Yvbe3171661j+ZqxZBb4xuxWJE71ZBb4xuxWJE71ZBb4xuxWJE32bGxoaYsWKFWzfvr3VoRxSym/dKMm3crSO5kTf5nzlR2vMmTOHefOyL3S/8pWv7MgPYt1JsFFO9G1s/ys//A87cYaGhtiyJfue35YtWzpy27uTYKOc6NuYr/xonfJtHxEdt+3dSbByTvRtzFd+tE6nb3t3EqycE30bW7JkCVOmZN9pmzJliq/8mECdftVNpx+orLGc6NtYb28v+/btA7Jema/8mDi9vb2M/kLrpEmTOm7bd/qByhrLid6sgjlz5rB06VIksXTp0o676qbTD1TWWLl/j94mXn9/P5MmTWLfvn1MmjSJ/v5+LrnkklaHdcjo7e1l06ZNHZkkRw9Uq1at6sgDVTvLey+MUqkEQHd3d9W6zb4Xhnv0bWz16tWMjIwAMDIy4nHWCTZnzhyuvvrqjk2Svb29nHTSSR15oCqCPXv2sGfPnlaHAbhH39aWLFnCrbfeyt69e9tunLXRvRrf3as2ebb/6La//PLLq7bn7Z9f3u00Wq+vr6+Z4eTiRN8ief5R9+7d++KVEyMjIwwMDIy5k7XrP2q79Gj2V0uibIdT7/Fo121vE8+Jvo1NnTqVKVOmMDIywuzZs1+8iqIddGKvplbtnCjzbP9O3vbWWE70LZI3UX7yk59k06ZNXHfddR07VtyOnChb61A4o2onTvRtburUqSxevNhJ3g457XxG1Wmc6M1swvmMamL58kozs4KrmuglfVPSVkkPl5XNlrRa0kB6PDqVS1KfpEFJ6yWd2szgzcysujw9+m8D792v7FLgjohYDNyR5gGWAovT33LgmsaEaWZm41U10UfEL4Cn9yteBoz+7mk/cE5Z+Q2RuRuYJen4RgVrZma1G+8Y/bER8QRAepybyucBm8vqlVKZmZm1SKM/jFWFsqhYUVouaY2kNdu2bWtwGGZmNmq8if6p0SGZ9Lg1lZeA+WX1uoEtlRqIiGsjoicierq6usYZhpmZVTPe6+hXAb3AFenx5rLyiyXdCJwO7Bwd4rHOkfcHy/IYGBgA8n8TuBp/A9Kaqaj7ftVEL+l7wLuAOZJKwN+SJfibJF0IPA6cm6rfCpwNDALDwMfGFZW11ODgIGsfWQuzGtBYdoMs1v5hbf1t7ai/iXZX1ETTKQYHB/ntwxuZf8Rxdbc1dSQbMBl+7Jm629q868m6lq+a6CPigjGeOrNC3QAuqisiaw+zYN+79rU6ipeZdFf1kcZ2TpRQPVkODg7yyEMbmTVj7ph18tr3fPaR2R9+v73utnYMb61eqSDmH3Ecf3Vae/VR/8u936pref8EQhO0c7Ipeq9scHCQ36xbR/39sZc+wNqxbl0DWoO8fbJZM+by7ted35DXbJQ7f3Njrnre99uTE30TDA4O8ruHH2DBzBfqbuuwvVm6eXbTfXW39fjuyXW30QmOAy6seAFYa11f+QK0QhkcHOThBx/kiMPqTy0jI9n/z2MbH6m7rV3Pj9TdRidzom+SBTNf4PM9u1sdxst8ac3MVodgh4AjDpvCacce3eowXubep+ofJ+9kTvR2gFKpBDvzjYlPqB1QilKro7ACK5VK/HHXrrrHxBtt864nObz0x3Ev70RvhVIqldhFew6TPAHsLh38QFUqldg5vCv3mPhE2TG8lShV/334UqnErudH2q4Hvev5kRdvZHIocqK3A3R3d7NN29ryqpvuedXvNmQ2Xt3d3Qy/8ExbXnUzo3v8w2FO9E2Qnf5Nbrsx8cd2Tebwgvdquru72TE01LYfxs6qclu87u5u9Nz2trzqZl539bucdXd388KunW05Rp/nloRF1WaDsGZm1mju0TdBd3c3z4480ZZX3UzL26vZ0aAPY0c3QSNObnbg30I1GwcnejvAokWLGtbW6JdeFs9bXH9j8/LF9iSN+TB29Pukjbot+5M05lcl2l2jPowdTtfRz5hS//c/fB29NcXjuxszRv/UcNarPnZG/R+MPr57Mq/NUa+R3x6c6Bs8N/IgtS0dpGYtbsBBiizJ54lvx/DWhlx1s/vZLNnOnFb/ePmO4a3My3HIa0Yn4YQGbf+8sW3e9WRDLq/cOpzdr2nujNl1t7V515OciD+MbSuN3NmfTzv7tIX17+yvpbGxtaNOPkhBoxNllmjmvab+c5J5HJMrNm//l+wdGAJgxgn1H2hP5Oi6YnOib4JO39mtdbzvtFZRt7+vujEzKzgnejOzgnOiNzMrOCd6M7OCc6I3Mys4X3VjZhMuz52oarnD1KF896g8nOjNOtChkCinT5/e6hAqynu7xHba/k70LdLonaUd/1Gttdo1UUJjr1dvV+20/ZuS6CW9F/gaMBm4LiKuaMbrHAraaWex9nEoJMp21YnbXhGNvROPpMnA74AlQAm4D7ggIjaMtUxPT0+sWbOmoXFYc9V6RrK4yu+VTPQZSS1DH9ViB59RWWtIuj8ieqrVa0aP/jRgMCIeTYHcCCwDxkz0VlydfEbSybGblWtGj/6DwHsj4t+n+Q8Dp0fExfvVWw4sB1iwYMGbH3vssYbGYWZWdHl79M24jr7SPdwOOJpExLUR0RMRPV1dXU0Iw8zMoDmJvgTML5vvBrY04XXMzCyHZiT6+4DFkl4l6TDgfGBVE17HzMxyaPiHsRExIuli4Gdkl1d+MyIeafTrmJlZPk25jj4ibgVubUbbZmZWG/+omZlZwTnRm5kVnBO9mVnBNfwLU+MKQtoGNPMbU3OAoSa232yOv3U6OXZw/K3W7PhPiIiqX0Rqi0TfbJLW5Pn2WLty/K3TybGD42+1donfQzdmZgXnRG9mVnCHSqK/ttUB1Mnxt04nxw6Ov9XaIv5DYozezOxQdqj06M3MDlkdl+glHSNpXfp7UtIfyuYPq1B/tqRP5Gh3iqQdzYk6H0m795v/qKSvp+nLytb1YUnvb0F8H5AUkl6X5hdK+rOy50+WdHYd7W+SNKcBcb6QttMjkh6UdImkqvu6pL9Py/x9vTE0iqTPpZjWp3U6vc72Pi3pWUlHlZW97H1L+9pn6nmd1E5DYk/72R5JayVtlHSvpN6cy34vvf6nx/PaZe009H2o8lp3SWrolTodd3PwiNgOnAzZDgnsjoivHGSR2cAngG80P7qmuyoiviLp9cAvJc2NiH0T+PoXAL8i+0XSy4CFwJ8B303Pnwz00PrfOdoTEaP7yFyy+I4C/rbKch8HuiLiuSbHl4uktwLvA06NiOfSQfCAzkyNLiD7hdkPAN9OZQ1/35oQ++8j4pTU9quBH0maFBHfOkgMxwFvi4gT6njdZr0PE6rjevQHI+mzqbf7sKQVqfgK4MR0FL5C0pGSfi7pgXR0fl8rYx6PiNgIjJB9GWNCSJoJvB24kCzRQ7Zt35m27V8DXwDOS/PnSTpN0q9TT+zXkk5MbU2W9BVJD6X3YMV+rzVd0k8l/UW9cUfEVrI7mV2szOTUc78vvfbH02uuAg4H7kmxd0n6Yap3n6S3p3qXSfpm6nU9KmllKj9c0k/SGcTDks5L5W+W9H8k3S/pZ5KOryH844Gh0QNPRAxFxJbU7iZJV6be7b2SFlVrTNJrgJnA58kSPsrOgl/2vu23zF9Iuk1SrfdVbGjs5dJtSi8Byrf9N9P7tFbSslT1dmBuWq931hh/1XUZaz0Osu9UjDPt7zem/fH7QOPvYRkRHftH1qv8TJo+DXgQmAEcAWwETgIWAevKlpkKHJGm5wIDaXoKsKPF6/MCsK7s73Hg6xXW9XSym7loAmP7EHB9mv41cCrwLuCWsjofHY03zR8JTEnT7wF+mKY/Cfyw7LnZ6XET2VnC/wY+UkesuyuUPQMcS5b0P5/KXgGsAV61/3JkZwHvSNMLgI1l78Ov07JzgO1pn/pT4B/Llj8qlf+a7CwB4Dyyn+3Oux4z037wO+AfgH9T9twm4HNp+iPl78NB2vs88J/IOnibgLljvG+XAZ8BLia7l8QrxvEeNCz2tE88vF/ZLLIzN4D/DHyorPx3ZAftA5Yb5/5UcV3GWo+D7DtjxXnJ6H5BlrNGgJ564y7/67ihm4N4J1kiGQaQ9L+Ad5Ad1csJuFLSO4B9wPx0KtbS8fnkxSEHyMboyU6pR31a0oeAXcB5kfaMCXIB8NU0fWOa/0mVZY4C+iUtJrud5NRU/h7gGxExAhART5ctczPw5Yj4TqMCT0ZvcXkWcJKyexuPxrgY+H/71X8P8AbpxTtjHinpiDT9k8h6d89J2kp2AHkI+IqkK8n+4X8p6Y3AG4HVqZ3JwBN5A46I3ZLeTLZvvxv4vqRLI+Lbqcr3yh6vytHk+cAHImKfpB8B5wL/bYy6Hya7W9w5EbE3b8xNjH1/5bcsPQt4v176XGEaWYLdM452DzDWuqSnK63HWPvOWHH+CdCXXmu9pPWNiLtckRJ9pXvVVvIRsn/uUyO7SUqJbIN3gqvi4J9HNIWkY4AzgDdKCrKEFVQf0/0icGdEfEDSQuCu0SapcB/h5J+BpZK+26gDmbIx3ReArem1V0TEz6osNgl4a0S8LFmkf97yMfwXyM5MfpeSwdnA30m6Hfgx8EhEvHW8sUfEC2Tb7S5JDwG9vDS2Xr59DrqtJJ1EdkAbPegcBjzK2In+YbKx+24OPAhOaOxjOIXsrB2y9/RPI+K35RXSPtcQY6wLVF6PsfadseLcv52GK9IY/S+AD6TxrpnAMuCXZL3fI8rqHQVsTUl+CTBv4kPtOB8EboiIEyJiYUTMJ/vn38fLt22lbf2HNP3RsvLbgU9ImgLZlVFlz/0N2XDIPzQicEldZB/Efz0dOH4GfFLS1PT8ayUdXmHR28mGLkbbOblCnfLXeSUwHBH/A/gK2dDWb4EuZR/mIWmqpH9VQ+wnprOhUSfz8h//O6/s8f9Wae4C4LL0/i2MiFcC8ySdwIHvG8Basg+nV6V1q0mDY9+/7YVk2/jqVPQzYEVKpEg6pdZ4q7zewdal0nqMte+MFecvgD9PZW8kG75pqMIk+oi4l+z06T7gbuCaiHgoIp4C1ij74O8K4J+At0laQ3bqOtCyoDvHBWS903I/JBsKGFH2AeSngTvJTllHP9T7Mlnv9p/JzgJGXUf2+cN6SQ+SXblT7i+BaZK+PM54p6cYHiEb778duLzstTcAD0h6GPjvVD6zXQn0pA/INpBduXUw/xq4V9I64HPAlyLiebKD5JVpPdcBb6thPWaSDX1tSKfzbyAbPx/1Ckn3AJ8CPg0g6f2SvlChrfM58D38cSrf/30DICJ+RTZW/xPVftlrI2MHeE36AHMjcBNwdbx0xc0XyYYF16f39Is1xlrPuhywHoy974wV5zXAzNT2Z4F7Gxy/vxlr1okkbSL7wK7jfsK3k2Mv10nrUZgevZmZVeYevZlZwblHb2ZWcE70ZmYF50RvZlZwTvRmZgXnRG9mVnBO9GZmBff/Af/NhFFFi4aDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "focusOn=df.drop(['Stage','Legendary'],axis=1)\n",
    "sns.boxplot(data=focusOn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python36\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ea5953e978>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(focusOn.Total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python36\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ea5bdc83c8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.boxplot(df.Legendary)\n",
    "sns.distplot(df.Stage)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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